The US Department of Education is currently considering a framework of regulations (PDF)—known as gainful employment (GE) rules—that would deny federal grant and loan aid to occupation-focused postsecondary programs if they don’t produce strong outcomes for graduates. One measure under consideration would have programs lose Title IV aid eligibility if the median earnings of their graduates don’t exceed the median earnings of young adults (ages 25–34) who have only a high school degree in the state where the program is located.
This proposed GE measure is intuitive because a higher education program should help its graduates earn at least what high school graduates do. But differences in enrollment and postdegree earnings by gender complicate this metric.
Women are more likely than men to pursue higher education and earn less than men with comparable levels of education on average. This dynamic makes the high school earnings comparison more difficult, as it may set an inappropriate bar for places and occupations where this gender imbalance is most prominent.
A majority of high school graduates in the labor force are men
To understand this proposed metric, we first must establish the differences in postsecondary enrollment and median pay by gender. According to my analysis of 2019 American Community Survey data, about 57 percent of 25-to-34-year-olds with only a high school diploma were men. Looking only at those in the labor force (employed or looking for employment, or with any earnings), this share jumps to 61 percent.
The median earnings gap between men and women with only a high school degree is large—about $8,800, or 43 percent of the average earnings for women. This median wage gap remains even as education level increases, but the percentage difference decreases to 26 percent at the master’s degree level. And as education levels go up, women are more likely to be overrepresented in the working population with higher education degrees relative to men.
Disparities in pay by gender can be explained by many factors, including occupational segregation into lower-paying fields, differences in schedule flexibility (PDF), and gender discrimination. But in the case of a benchmark for college quality that uses the typical wages for high school graduates, the gender wage gap poses a double-barreled problem: median high school wages are more likely to contain the higher wages of men, while wages of those who earn degrees are more likely to contain the lower wages of women.
Wage inequities cut across and against higher education for women
Programs that do not pass the GE high school earnings threshold predominantly graduate women. The typical graduate cohort for a program that fails the earnings threshold for high school graduates in the labor force is 83 percent female. Among programs that pass, the cohort is 55 percent female. Results are similar when comparing to the second proposed measure, the earnings of all people with a high school diploma (although fewer programs overall fail the threshold).
Many of the programs that fail these measures are cosmetology or therapeutic programs, both of which predominantly enroll women. It is possible these programs are driving my results and that remaining programs that get “caught” by these thresholds are more balanced in gender. But even when cosmetology or therapeutic programs are excluded, the programs that don’t meet the threshold still serve higher shares of women than programs that pass.
Programs That Do Not Meet the High School Diploma Threshold Are Predominantly Female
|
|
Average Share of Women Earning Awards, Positive Earnings Threshold |
Average Share of Women Earning Awards, All High School Graduates Threshold |
||||
|
Credential level (IPEDS) |
Below |
Failed (w/ exclusions) |
Passed |
Below |
Below (w/ exclusion) |
Above |
|
01 - Undergraduate certificate (includes diploma) |
84 |
78 |
52 |
85 |
74 |
53 |
|
02 - Associate's degree |
74 |
74 |
62 |
66 |
65 |
63 |
|
03 - Bachelor's degree |
67 |
67 |
57 |
64 |
64 |
57 |
|
05 - Master's degree |
72 |
72 |
63 |
67 |
67 |
63 |
Source: Integrated Postsecondary Education Data System (IPEDS) award information and 2022 gainful employment (GE) earning data.
Notes: “w/ exclusions” excludes programs in the fields of cosmetology and related personal grooming services and somatic bodywork and therapeutic services. Programs are counted only if they are subject to GE regulations. To conduct this analysis, I linked IPEDS award information by gender to 2022 GE earnings data, matching about 83 percent of GE programs with median earnings data.
Because the threshold high school earnings data are broken out by state, we can spot some of these gender differences intuitively. The three states with the highest GE earnings thresholds—Wyoming, North Dakota, and New Hampshire—do not generally have a high cost of living. But these states do have disproportionately high shares of high school graduates working in male-dominated occupations (agriculture, forestry, fishing, and hunting, and mining in Wyoming and North Dakota and construction in New Hampshire).
How policymakers can refine earnings accountability measures
Policymakers should not abandon the idea of using an earnings threshold for higher education accountability. Students should be protected from enrolling in programs that would leave them the same or worse off than if they had not enrolled. And accountability measures shouldn’t be adjusted based on the demographics of those who are enrolled, because this would remove consumer protections for students who may need it the most.
But policymakers can think more carefully about the comparison earnings threshold they select. Some alternative policies include the following:
Tying an earnings threshold to local measures: The proposed GE earning thresholds are somewhat divorced from relative cost of living or minimum wages. For example, New York has the highest cost of living in the contiguous United States but falls in a middle-to-low rank on the high school earnings threshold. And Washington, DC, has the highest minimum wage but is among the lowest in earnings threshold metrics. Policymakers could opt for a measure more closely tied to local prevailing wages or cost of living, rather than the earnings of a given group, to better capture differences in outcomes relative to local conditions.
Balancing earnings against tuition: My colleagues suggest a tuition-to-earnings ratio that might better account for the costs students (and taxpayers) take on when enrolling in a program. This metric would ensure students are protected from investing in a program that might not pay off relative to the cost but are still able to enroll in programs that grant access to a desired career.
Using data on preenrollment earnings: Most students provide information on their household earnings or their family’s earnings when applying to postsecondary programs. Policymakers could consider an economic mobility measure to determine if students are better off than before they started the program. This option might only be possible for independent students, but a measure like this could help policymakers better estimate the counterfactual wage for those who enter these programs.
An earnings threshold based on the income of high school graduates could yield unequal effects because of gender-based disparities in postsecondary enrollment and median pay.
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